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Kernel Methods

571 directly classified papers

Papers per year

Papers

The Nyström method for convex loss functions JMLR 2024
Low-Rank Kernel Tensor Learning for Incomplete Multi-View Clustering AAAI 2024
Recursive Estimation of Conditional Kernel Mean Embeddings JMLR 2024
Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions NIPS 2024
On Regularized Radon-Nikodym Differentiation JMLR 2024
Random Smoothing Regularization in Kernel Gradient Descent Learning JMLR 2024
Dense Associative Memory Through the Lens of Random Features NIPS 2024
Differential Privacy in Scalable General Kernel Learning via $K$-means Nystr{\"o}m Random Features NIPS 2024
Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces JMLR 2024
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case JMLR 2024
On the Optimality of Misspecified Spectral Algorithms JMLR 2024
Geometric Learning with Positively Decomposable Kernels JMLR 2024
Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification AAAI 2024
Error Estimation for Random Fourier Features AISTATS 2023
Overparameterized Random Feature Regression with Nearly Orthogonal Data AISTATS 2023
Kernel QuantTree ICML 2023
Simplex Random Features ICML 2023
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms ICML 2023
Optimal Convergence Rates for Agnostic Nyström Kernel Learning ICML 2023
Consistency of Multiple Kernel Clustering ICML 2023
Nearly-tight Bounds for Deep Kernel Learning ICML 2023
Towards a Persistence Diagram that is Robust to Noise and Varied Densities ICML 2023
Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces JMLR 2023
Optimal Convergence Rates for Distributed Nystroem Approximation JMLR 2023
Isotropic Gaussian Processes on Finite Spaces of Graphs AISTATS 2023